Modeling performance of response surface methodology and artificial neural network

S.O Sada

doi:10.4314/jasem.v22i6.6

Abstract

In recent years, response surface methodology (RSM) which is a statistical technique and artificial neural network (ANN) a soft computing technique have been highly used for modelling, simulation and optimization of several physical processes in engineering. Both RSM and ANN strategies have particular computational properties that makes them suitable for making predictions, but differ in their extrapolation and interpolation capabilities on complex non-linear processes, and thus potentially conflict in their predictive accuracy. This study models and compares the capabilities of RSM and ANN in predicting the tensile strength of a 6 mm thick mild steel gas tungsten arc welded plate based on the effects of input variables such as weld current, weld speed, gas flow rate and filler rod. The RSM and ANN based models for prediction were compared using the coefficient of determination criteria. With a higher value of 0.836, the ANN model proved to be a better modeling technique than the RSM model. Keywords: Soft Computing Techniques, Response Surface Method, Artificial Neural Network

Highlights

This study models and compares the capabilities of response surface methodology (RSM) and artificial neural network (ANN) in predicting the tensile strength of a 6 mm thick mild steel gas tungsten arc welded plate based on the effects of input variables such as weld current, weld speed, gas flow rate and filler rod
Recent studies, have shown that response surface methodology (RSM) which is a statistical technique and artificial neural network (ANN) a soft computing technique have been highly used for modelling, simulation and optimization of several physical processes in engineering
The experimental responses in RSM are fitted to a quadratic function and one of its advantages is its ability to optimize a process and interpret the interactive effects of the process variable on the response using a lesser number of experiments

Summary

Introduction

Recent studies, have shown that response surface methodology (RSM) which is a statistical technique and artificial neural network (ANN) a soft computing technique have been highly used for modelling, simulation and optimization of several physical processes in engineering. Advantages over the conventionally followed one factor-at-a-time approach, and are considered an effective modelling tool for solving complex nonlinear multivariable systems They don’t require the explicit expressions of the physical meaning of the system or process under investigation. This study models and compares the capabilities of RSM and ANN in predicting undercut weld defects in a Gas Tungsten Arc Welded mild steel rod

Methods

Findings

Conclusion